It is well known that chromatography and electrophoresis are extensively used for estimating concentration of the constituents of a mixture. For instance, analytical chromatography is used for measuring the relative proportions of analytes in a mixture. The essence of all chromatography methods is the partition of analytes between a stationary phase and a mobile phase which elutes through the stationary phase. Further, in high performance liquid chromatography (HPLC), an analyte is generally adsorbed onto an adsorbent in the column. An eluent or solvent selectively removes or displaces analytes from the column, and differences in the partition coefficients result in separation of analytes along the length of the column. The quantity of each analyte is measured as it exits the column, by passing the column output through a detector. The chromatography data is a signal plotted against time (which is generally referred to as chromatogram), where the height of the signal represents the extent of detection of a constituent at that point of time. The type of signal value depends on the type of the detector employed in the analyzer, which exploits a specific physical or chemical property of the mixture.
The area under such a chromatogram gives a measure of the concentration of the constituents. This integrated signal produces the typical chromatogram, which is a plot of signal versus time, and usually appears as a series of peaks. Each peak area yields the amount of the corresponding analyte. The location of the peak indicates the analyte in question. When the peaks are well separated, the areas corresponding to different analytes are distinct and can be correlated well with the amounts of the different analytes in the mixture. However, there are situations where there are unresolved components, as shown in FIG. 1, due to different constituents having similar retention times in the separation column as a result of which they are not fully separated.
In addition, noise may be present which makes it more difficult to separate the peaks. In such a case, the peaks are poorly separated and resolution of areas is difficult or impossible.
Resolution is related to column efficiency, and therefore there is a constant endeavor to increase the efficiency. This is motivated by the fact that higher column efficiency implies improved resolution. Chromatographic efficiency depends on many experimental variables, including temperature, pressure, length of the chromatographic column, and eluent flow rate. Optimizing these variables for a given experiment is a challenging task. Traditional laboratory experiments involve systematic iterations of chromatography, in order to obtain well resolved and separated peaks in a chromatogram. This is tedious and generally time consuming, with no guarantee of success.
Similar is the case with electrophoresis, there are situations where there are unresolved components and for increasing the efficiency, experimental variables such as pH of the buffer, voltage/power employed, length of the gel or time used during electrophoresis have to be optimized. Again optimizing these variables for a given experiment is challenging, tedious and time consuming.
Moreover, in recent years, there have been considerable attempts in employing computational techniques to achieve the optimization task. Early attempts involved using least squares curve fitting. More recent approaches involve optimizing method parameters. Prediction of retention times using support vector machines was also attempted. In the context of gas chromatography, computer simulations to model retention times by using a linear elution strength approximation were also tried. However, such optimization techniques require many experiments and simulations. Further, experiments, when manually conducted, are tedious and time consuming. Moreover, there is no guarantee that the output would be properly separated peaks.
Therefore, there always existed a need in the art to provide a technique for determining the unknown concentration of the constituents of a mixture, that is simple, accurate, easy to implement and at the same time overcomes the above mentioned disadvantages of the prior art.